kalman-simulink

快速区域生长算法，已测试相当不错，欢迎感兴趣的达人们下载测试(Fast region growing algorithm has been tested pretty well, welcome to download the test of interest up to the people)

异步电机直接转矩控制的仿真，matlab(Induction Motor Direct Torque Control, matlab)

介绍一个稳健性分析工具箱。主要做稳健性主成分、主成分回归、分类。(Our toolbox currently contains implementations of robust methods for location and scale estimation, covariance estimation (FAST-MCD), regression (FAST-LTS, MCD-regression), principal component analysis (RAPCA, ROBPCA), principal component regression (RPCR), partial least squares (RSIMPLS) and classification (RDA). Only a few of these methods will be highlighted in this paper. The toolbox also provides many graphical tools to detect and classify the outliers. The use of these features will be explained and demonstrated through the analysis of some real data sets.)

无人机一维模糊控制器设计，simulink下运行(UAV fuzzy)

matlab之语音处理与合成工具箱的随书程序！！！Good!!!It s useful for you !!!(Good!!!It s useful for you !!!)

说明：  垂直阵测量误差分析及甚宽频带恒定束宽性能，吴国清的论文(Vertical array analysis and measurement error is very broadband constant beamwidth performance, Wu Guoqing papers)

ieee 30 bus test system in simulink

Filtros Ma t lab trae varios filtros para series de tiempo

sliding mode controller slotine example 7-2

Photos with people (e.g., family, friends, celebrities, etc.) are the major interest of users. Thus, with the exponentially growing photos, large-scale content-based face image retrieval is an enabling technology for many emerging applications. In this work, we aim to utilize automatically detected human attributes that contain semantic cues of the face photos to improve contentbased face retrieval by constructing semantic codewords for efficient large-scale face retrieval. By leveraging human attributes in a scalable and systematic framework, we propose two orthogonal methods named attribute-enhanced sparse coding and attributeembedded inverted indexing to improve the face retrieval in the offline and online stages. We investigate the effectiveness of different attributes and vital factors essential for face retrieval. Experimenting on two public datasets, the results show that theproposed methods can achieve up to 43.5 relative improvement in MAP compared to the existing methods.